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This study examined the single-nucleotide polymorphism heritability and genetic correlations of cognitive abilities and brain structural measures (regional subcortical volume and cortical thickness) in middle-aged and elderly East Asians (Korean) from the Gwangju Alzheimer’s and Related Dementias cohort study. Significant heritability was found in memory function, caudate volume, thickness of the entorhinal cortices, pars opercularis, superior frontal gyri, and transverse temporal gyri. There were 3 significant genetic correlations between (i) the caudate volume and the thickness of the entorhinal cortices, (ii) the thickness of the superior frontal gyri and pars opercularis, and (iii) the thickness of the superior frontal and transverse temporal gyri. This is the first study to describe the heritability and genetic correlations of cognitive and neuroanatomical traits in middle-aged to elderly East Asians. Our results support the previous findings showing that genetic factors play a substantial role in the cognitive and neuroanatomical traits in middle to advanced age. Moreover, by demonstrating shared genetic effects on different brain regions, it gives us a genetic insight into understanding cognitive and brain changes with age, such as aging-related cognitive decline, cortical atrophy, and neural compensation.

Established genetic risk factors for Alzheimer’s disease (AD) account for only a portion of AD heritability. The aim of this study was to identify novel associations between genetic variants and AD-specific brain atrophy. We conducted genome-wide association studies for brain magnetic resonance imaging measures of hippocampal volume and entorhinal cortical thickness in 2643 Koreans meeting the clinical criteria for AD (n = 209), mild cognitive impairment (n = 1449) or normal cognition (n = 985). A missense variant, rs77359862 (R274W), in the SHANK-associated RH Domain Interactor (SHARPIN) gene was associated with entorhinal cortical thickness (p = 5.0 × 10−9) and hippocampal volume (p = 5.1 × 10−12). It revealed an increased risk of developing AD in the mediation analyses. This variant was also associated with amyloid-β accumulation (p = 0.03) and measures of memory (p = 1.0 × 10−4) and executive function (p = 0.04). We also found significant association of other SHARPIN variants with hippocampal volume in the Alzheimer’s Disease Neuroimaging Initiative (rs3417062, p = 4.1 × 10−6) and AddNeuroMed (rs138412600, p = 5.9 × 10−5) cohorts. Further, molecular dynamics simulations and co-immunoprecipitation indicated that the variant significantly reduced the binding of linear ubiquitination assembly complex proteins, SHPARIN and HOIL-1 Interacting Protein (HOIP), altering the downstream NF-κB signaling pathway. These findings suggest that SHARPIN plays an important role in the pathogenesis of AD.

Although depression is an emerging disorder affecting many people worldwide, most genetic studies have been performed in European descent populations. Herein, a genome-wide association study (GWAS) was conducted in Korean population to elucidate the genomic loci associated with depressive symptoms. Two independent cohorts were used as discovery datasets, which consisted of 6 474 (1 484 cases and 4 990 controls) and 1 654 (557 cases and 1 097 controls) Korean participants, respectively. The participants were divided into case and control groups based on the Beck Depression Inventory (BDI). Meta-analysis using the two cohorts revealed that rs6945590 was significantly associated with the risk of depressive symptoms [P = 2.83 × 10− 8; odds ratio (OR) = 1.23; 95% confidence interval (CI): 1.15–1.33]. This association was validated in other independent cohorts which were another Korean cohort (258 cases and 1 757 controls) and the East Asian study of the Psychiatric Genomics Consortium (PGC) (15 771 cases and 178 777 controls). The predicted expression levels of thromboxane A synthase 1 gene (TBXAS1), which encodes the enzyme thromboxane A synthase 1 and participates in the arachidonic acid (AA) cascade, was significantly decreased in the whole blood tissues of the participants with depressive symptoms. Furthermore, Mendelian randomization (MR) analysis showed a causal association between TBXAS1 expression and the risk of depressive symptoms. In conclusion, as the number of risk alleles (A) of rs6945590 increased, TBXAS1 expression decreased, which subsequently caused an increase in the risk of depressive symptoms.

To identify genetic variations which is associated with gastric cancer (GC) risk according to Helicobacter pylori infection.This study incorporated 527 GC patients and 441 controls from a cohort at Seoul National University Bundang Hospital. The associations between GC risk and single nucleotide polymorphisms were calculated, stratified by H. pylori status, adjusting for age, sex, and smoking. mRNA expression from non-cancerous gastric mucosae was evaluated using reverse transcription quantitative polymerase chain reaction.In the entire cohort, genome-wide association study showed no significant variants reached the genome-wide significance level. In the H. pylori-positive group, rs2671655 (chr17:47,468,020;hg19, GH17J049387 enhancer region) was identified at a genome-wide significance level, which was more pronounced in diffuse type GC. There was no significant variant in the H. pylori-negative group, indicating the effect modification of rs2671655 by H. pylori. Among the target genes of GH17J049387 enhancer (PHB1, ZNF652 and SPOP), PHB1 mRNA was expressed more in cases than in controls, who were not affected by H. pylori. By contrast, an increase in ZNF652 and SPOP in GC was observed only in the H. pylori-negative group (P 0.05). Mediation analysis showed that PHB1 (P = 0.0238) and SPOP (P = 0.0328) mediated the effect of rs2671655 on GC risk. The polygenic risk score was associated with the number of rs2671655 risk alleles only in the H. pylori-positive group (P = 0.0112).After H. pylori infection, rs2671655 may increase GC risk, especially in diffuse-type GC, by regulating the expression of several genes that consequently modify susceptibility to GC.

Genome-wide association studies of gastric cancer (GC) cases have revealed common gastric cancer susceptibility loci with low effect size. We investigated rare variants with high effect size via whole-exome sequencing (WES) of subjects with familial clustering of gastric cancer. WES of DNAs from the blood of 19 gastric cancer patients and 36 unaffected family members from 14 families with two or more gastric cancer patients were tested. Linkage analysis combined with association tests were performed using Pedigree Variant Annotation, Analysis, and Search Tool (pVAAST) software. Based on the logarithm of odds (LOD) and permutation-based composite likelihood ratio test (CLRT) from pVAAST, MUC4 was identified as a predisposing gene (LOD P-value = 1.9×10-5; permutation-based P-value of CLRT ≤ 9.9×10-9). In a larger cohort consisting of 597 GC patients and 9,759 healthy controls genotyped with SNP array, we discovered common variants in MUC4 regions (rs148735556, rs11717039, and rs547775645) significantly associated with GC supporting the association of MUC4 with gastric cancer. And the MUC4 variants were found in higher frequency in The Cancer Genome Atlas Study (TCGA) germline samples of patients with multiple cancer types. Immunohistochemistry indicated that MUC4 was downregulated in the noncancerous gastric mucosa of subjects with MUC4 germline missense variants, suggesting that loss of the protective function of MUC4 predisposes an individual to gastric cancer. Rare variants in MUC4 can be novel gastric cancer susceptibility loci in Koreans possessing the familial clustering of gastric cancer.

Motivation Despite the need for separate tools to analyze family-based data, there are only a handful of tools optimized for family-based big data compared to the number of tools available for analyzing population-based data. Results ONETOOL implements the properties of well-known existing family data analysis tools and recently developed methods in a computationally efficient manner, and so is suitable for analyzing the vast amount of variant data available from sequencing family members, providing a rich choice of analysis methods for big data on families. Availability and implementation ONETOOL is freely available from http://healthstat.snu.ac.kr/software/onetool/. Supplementary information Supplementary data are available at Bioinformatics online.

Background Transcriptomic profiles can improve our understanding of the phenotypic molecular basis of biological research, and many statistical methods have been proposed to identify differentially expressed genes (DEGs) under two or more conditions with RNA-seq data. However, statistical analyses with RNA-seq data are often limited by small sample sizes, and global variance estimates of RNA expression levels have been utilized as prior distributions for gene-specific variance estimates, making it difficult to generalize the methods to more complicated settings. We herein proposed a Bartlett-Adjusted Likelihood-based LInear mixed model approach (BALLI) to analyze more complicated RNA-seq data. The proposed method estimates the technical and biological variances with a linear mixed-effects model, with and without adjusting small sample bias using Bartlkett’s corrections. Results We conducted extensive simulations to compare the performance of BALLI with those of existing approaches (edgeR, DESeq2, and voom). Results from the simulation studies showed that BALLI correctly controlled the type-1 error rates at various nominal significance levels and produced better statistical power and precision estimates than those of other competing methods in various scenarios. Furthermore, BALLI was robust to variation of library size. It was also successfully applied to Holstein milk yield data, illustrating its practical value. Conclusions; BALLI is statistically more efficient and valid than existing methods, and we conclude that it is useful for identifying DEGs in RNA-seq analysis. Electronic supplementary material The online version of this article (10.1186/s12864-019-5851-6) contains supplementary material, which is available to authorized users.

In genetic analyses, the term ‘batch effect’ refers to systematic differences caused by batch heterogeneity. Controlling this unintended effect is the most important step in quality control (QC) processes that precede analyses. Currently, batch effects are not appropriately controlled by statistics, and newer approaches are required. In this report, we propose a new method to detect the heterogeneity of probe intensities among different batches and a procedure for calling genotypes and QC in the presence of a batch effect. First, we conducted a multivariate analysis of variance (MANOVA) to test the differences in probe intensities among batches. If heterogeneity is detected, subjects should be clustered using a K-medoid algorithm using the averages of the probe intensity measurements for each batch and the genotypes of subjects in different clusters should be called separately. The proposed method was used to assess genotyping data of 3619 subjects consisting of 1074 patients with Alzheimer’s disease, 296 with mild cognitive impairment (MCI), and 1153 controls. The proposed method improves the accuracy of called genotypes without the need to filter a lot of subjects and SNPs, and therefore is a reasonable approach for controlling batch effects. We proposed a new strategy that detects batch effects with probe intensity measurement and calls genotypes in the presence of batch effects. The application of the proposed method to real data shows that it produces a balanced approach. Furthermore, the proposed method can be extended to various scenarios with a simple modification.

Estimated type-1 error rates with simulation data for N = 4, 6, 8, 28, 40, 64, and 68 based on simulated data from negative binomial distribution. (DOCX 21 kb)

Estimated powers and precisions with simulation data when δ = 0.5σ or 1σ and N = 12, 16 and 20 based on Holstein cow’s data. (DOCX 197 kb)

Estimated type-1 error rates with simulation data for N = 4, 6, 8, 12, 16 and 20 based on Holstein cowâ s data. (DOCX 20 kb)

Analysis of covariables, parity and lactation period, for true nine DEGs of Holstein cow's data. (DOCX 18 kb)

Computation time for each method when analyzing Holstein cowâ s data. (DOCX 14 kb)

Effect of varying library sizes on the statistical power and precision when u = 1, 0.8, 0.6, or 0.4, δ = 1Ď and N = 12, 16, 20, 24, 28, 40, 64, or 68. (DOCX 593 kb)

Effect of varying library sizes on the type-1 error rates when u = 0.2, 0.4, 0.6, 0.8, and 1 and N = 12, 16, 20, 24, 28, 40, 64, or 68 at the 0.005 nominal significance level. (DOCX 371 kb)

Estimated type-1 error rates with simulation data for N = 4, 6, 8, 28, 40, 64, and 68 based on Nigerian people’s data. (DOCX 21 kb)

Steps of generating simulation data. (DOCX 18 kb)

MotivationTranscriptomic profiles can improve our understanding of the phenotypic molecular basis of biological research, and many statistical methods have been proposed to identify differentially expressed genes under two or more conditions with RNA-seq data. However, statistical analyses with RNA-seq data often suffer from small sample sizes, and global variance estimates of RNA expression levels have been utilized as prior distributions for gene-specific variance estimates, making it difficult to generalize the methods to more complicated settings. We herein proposed a Bartlett-Adjusted Likelihood based LInear mixed model approach (BALLI) to analyze more complicated RNA-seq data. The proposed method estimates the technical and biological variances with a linear mixed effect model, with and without adjusting small sample bias using Bartlett’s corrections.ResultsWe conducted extensive simulations to compare the performance of BALLI with those of existing approaches (edgeR, DESeq2, and voom). Results from the simulation studies showed that BALLI correctly controlled the type-1 error rates at the various nominal significance levels, and produced better statistical power and precision estimates than those of other competing methods in various scenarios. Furthermore, BALLI was robust to variation of library size. It was also successfully applied to Holstein milk yield data, illustrating its practical value.Availability and ImplementationBALLI is implemented as R package and freely available at http://healthstat.snu.ac.kr/software/balli/.Contactwon1@snu.ac.krSupplementary InformationSupplementary data are available at Bioinformatics online

SummaryBackground Vitamin D receptor (VDR) is expressed in dermal papilla cells and in the epidermis of the hair follicle. Patients with alopecia areata (AA) and alopecia universalis (AU) have VDR mutations. Aims We investigated expression of VDR in isolated hair follicles and epidermal keratinocytes from patients with AA or AU, and assessed changes in the expression of Wnt signalling regulation factors to determine the relationship between the occurrence of AA or AU and decreased expression of VDR. Methods Immunohistochemistry was performed for canonical Wnt signalling molecules, VDR, and proliferation and differentiation markers in the skin tissue of patients with AA or AU. After VDR small interfering RNA or Dickkopf-1 (DKK1) treatment of follicle dermal papilla (DP) cells, expression of VDR and Wnt signalling molecules was determined. DKK1 was used to treated keratinocytes and DP cells in a transwell coculture system. Western blotting analysis was performed to assay for VDR and β-catenin. Results Expression of Wnt/β-catenin signals and VDR was decreased in AA or AU lesions compared with nonlesional skin. Inhibition of Wnt/β-catenin signals and VDR influenced differentiation and proliferation of epidermis and hair follicles. In the transwell coculture system, DKK1 reduced the expression of VDR and β-catenin in cells in a dose-dependent manner. Conclusions This study demonstrates that the decreased expression of VDR in AA and AU lesions is related to decreased expression of Wnt/β-catenin signals, which inhibits proliferation and differentiation of hair follicles and epidermal cells.

Agminated lentiginosis is an unusual pigmentary disorder, characterized by numerous lentigines grouped within an area of normal skin. The pigmented macules are often in a segmental distribution within a sharp demarcation at the midline. We encountered a 28-year-old woman with an unusual combination of multiple café-au-lait macules and diffuse numerous lentigines involving the right cheek and ipsilateral upper thorax with sharp demarcation at the midline. The multiple lentigines extended bilaterally over the back in a peppered distribution. There were 21 café-au-lait macules on both arms, and the trunk and buttocks; however, there were no Lisch nodules, neurofibromas, or any other clinical manifestations for neurofibromatosis. Histopathology of a macule revealed the features of lentigo.